Table 3 The location result of robust model chemical library screening with different κ. As is shown in Table 3, the location result of robust optimization model chose one more freight transport center than the expected optimization model, which means it needs more centers to make up the influence of stochastic demand.

The number of disadvantageous scenarios in expected value model is the maximum; there are 163 disadvantageous scenarios in total 300 imitation scenarios. Compare the results with different κ values, when κ increases the expected value of model increases, while the deviation value and the disadvantageous scenarios decrease. So the introduction of robust model improves transport capacity of the system, which makes the location result more reliable and more applicable. Furthermore, the increase of κ will decrease the deviation value, which needs more investment and causes the expected value to increase. In practice, the planners need to decide the index κ and balance the weight between expected value and deviation value. 6. Conclusion A robust optimization model is proposed to

mitigate the influence of disadvantageous scenarios which is caused by the stochasticity of the transport demand. The robust model is based on the deterministic model and expected optimization model. A new heuristic algorithm is proposed which combines CM with ACSA. The numerical example is implemented on a network. Computational results demonstrate the model and algorithm are available. And the robust model can help to improve the reliability of location decision. While there are some fluctuations such as transport cost, constructing cost that are not considered in the model. These aspects can be considered in the future research. Acknowledgments This work was supported by National Basic Research Program of China (no. 2012CB725403), National Natural Science Foundation of China (no. 61374202), and Research Project of China Railway Company (nos. 2014F007, 2013X005-A, and 2013F021). Conflict of Interests The authors declare that there is no conflict of interests regarding

the publication of this paper.
Increasing the capacity is one of the most important objectives for urban Entinostat traffic management at congested conditions [1]. After years of effort, there is little space to improve the optimization models of determining optimal lane allocations and signal timings for conventional intersections [2]. In this way, reorganizing traffic movements is one possible way to increase the capacity of urban intersections. The average delay or stop can be reduced by regulating the vehicles maneuver in an expected manner [3, 4]. Unconventional intersections such as median U-turns, jughandles, superstreets, continuous flow intersections, and bowties are most mentioned in the regulation [5, 6]. However, the unconventional design may not be available in urban road network due to the limitations of extra infrastructure.